You don’t have to be Nostradamus to predict that predictive analytics is going to become more and more important to digital marketers. Gartner sees an ongoing shift from analyzing historical descriptive data in aggregate to tell the story of “what happened,” to performing calculations on data sets to predict — with more or less confidence
Recently, various empirical and semi-empirical models embedded in different modeling tools have been developed and recognized for their role in predicting pharmacokinetics of drugs in humans. These models have also been used to evaluate the effects of...
The future of customer service is scary and rewarding at the same time. It’s scary because the machine will know everything about you. It’s rewarding because shopping will become much easier as the machine makes more decisions...
Three main hurdles holding back Predictive Analytics Marketplaces are a highly fragmented data mining tools market, limited support for customization, and lack of commitment. We examine how to overcome them. An earlier article on KDnuggets noted that...
This article will make you feel better. And you do need to feel better, if you are one of the many of us who practice analytics—or who must consume and rely on analytics—and find ourselves carrying tension...
Eric Siegel, PhD, founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times.com, makes the how and why of predictive analytics understandable and captivating. In addition to being the author...
In the never-ending quest for pre-qualified leads, increasing numbers of insurance companies are turning to predictive analytics to net potential customers from that great sea of people known as the general population. Popularized in the 2011 movie,...
Version 4.2 of the Predictive Model Markup Language (PMML), which aims to make it easier to develop predictive analytics apps, is now available. The Data Mining Group, a vendor-led consortium of companies and organizations developing standards for...
Before companies can profit from big data, they often must deal with bad data. There may indeed be gold in the mountains of information that firms collect today, but there also are stores of contaminated or “noisy”...
